Someone interested in many things.

  • 6 Posts
  • 65 Comments
Joined 1 year ago
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Cake day: June 15th, 2023

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  • should know this already. :)

    What in the gosh darn condescending non sequitur is that? I have a special kind of dislike for people who, instead of trying to promote learning for anyone and everyone at any stage, instead choose to ridicule people for having missed some trivial detail that has about as much in common with Bash as does COBOL (basically nothing). Web scripting is, unsurprisingly, its own skill, and it’s very, surpassingly, extremely, stupendously, and obviously conceivable that someone could have years of Bash experience but only recently started putting around with scripting for things like API access or HTML parsing. But you should know this already. :)








  • Like I said, I’m aware of extant measures to try and steer models, but people often assume a level of craftsmanship in censoring models that simply does not exist. Jailbreakchat.com is an endless stream of examples of this very fect; it’s very hard, especially with the limited context lengths of current models, to effectively give them any hard directives.

    And back to foundational models, which are essentially free of censorship, they will still exhibit a similar level of political bias unless prompted otherwise. All this to say that, discounting OpenAI’s attempts to control their models, the model itself will inherently learn from and mirror the real-world biases of the text it was trained on. Those biases happen to fall along lines that often ignore subtlety in debates regarding illegality and morality.


  • It’s hard to say what LLMs are “programmed” to do, as they’re largely untamed beasts of text prediction. In fact, I would suspect its built-in biases are less the result of pre-prompting or post-foundational-model training and really just what a lot of people tend to think online. In a way, it’s more like people in general often equate illegality with immorality.

    You can see similar biases in many of the open-source LLMs that are floating around. Even though they’re basically built outside of large corporate cultures and large-scale monetary incentive, they still retain a lot of political bias that tends to favor governmental measures heavily.









  • Oh, and for completeness:

    • We’ve deleted the vast majority of the spam bots that spammed our instance, are currently on closed registration with applications, and have had no anomalous activity since.

    • Our server is essentially always at 50% memory (1GB/2GB), 10% CPU (2 vCPUs), and 30% disk (15-20GB/60GB) until a spike. Disk utilization does not change during a spike.

    • Our instance is relatively quiet, and we probably have no more than ten truly active users at this point. We have a potential uptick in membership, but this is still relatively slow and negligible.

    • This issue has happened before, but I assumed it was fixed when I changed the PostgreSQL configuration to utilize less RAM. This is still the longest lead-up time before the spikes started.

    • When the spike resolves itself, the instance works as expected. The issues with service interruptions seems to stem from a drastic increase in resource utilization, which could be caused by some software component that I’m not aware of. I used the Ansible install for Lemmy, and have only modified certain configuration files as required. For the most part, I’ve only added a higher max_client_body_size in the nginx configs for larger images, and have added settings for an SMTP relay to the main config.hjson file. The spikes occured before these changes, which leads me to believe that they are caused by something I have not yet explored.

    • These issues occured on both 0.17.4 and 0.18.0, which seems to indicate it’s not a new issue stemming from a recent source code change.





  • I have a Twitter account, but I haven’t even signed into it or looked at Twitter in a month or more. It’s just a shittier version of Lemmy or Reddit where people need to actually bother getting followers to be heard, and they can only say things with little to no context. Not really a huge surprise that it’s the Waffle House of (not so) intelligent internet debates.